Microsoft EdgeML: EdgeML provides a collection of lightweight ML algorithms, operators, and tools aimed towards deployment on Class 0 devices, written in PyTorch and TF. Included algorithms include Bonsai [1]}, ProtoNN [2]}, FastRNN [3]}, FastGRNN [3]}, ShallowRNN [5]}, EMI-RNN [6]}, RNNPool [7]}, and DROCC [8]}. EMI-RNN exploits the fact that only a small, tight portion of a time-series plot for a certain class contributes to the final classification while other portions are common among all classes. Shallow-RNN is a hierarchical RNN architecture that divides the time-series signal into various blocks and feeds them in parallel to several RNNs with shared weights and activation maps. RNNPool is a non-linear pooling operator that can perform "pooling" on intermediate layers of a CNN by a downsampling factor much larger than 2 (4-8\(\times \) ) without losing accuracy while reducing memory usage and decreasing compute. Deep robust one-class classifier (DROCC) is an OCC under limited negatives and anomaly detector without requiring domain heuristics or handcrafted features. The framework also includes a quantization tool called SeeDot [9]}.
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